Optical character recognition (OCR) has a variety of applications. Existing OCR approaches typically use a single tool for detecting and recognizing characters within images. However, in some applications, these single tool approaches are insufficient to identify, isolate, and recognize special characters within a document.
As an example, business checks and personal checks commonly have bank codes and accounting information (MICR line) printed thereon with magnetic ink characters. This information is necessary for settling a check payment. OCR is desirable to capture this information without requiring human input. Using existing OCR approaches, which presume that this information is printed on a lower portion of a check, an OCR tool is used to read all the text in a check image; then utilizing image processing, the lower portion of the image and the text read in that region is interpreted and assumed to be the MICR line. However, when the MICR line is not in its expected location in the lower portion of a check, existing OCR approaches may inaccurately read MICR line, thus requiring human input to read and correct the error or, if the error is not caught and the misread MICR line corresponds to a different account, an improperly paid check may result.
Accordingly, there is a need for systems and methods for providing improved detection and recognition of specialized information, such as MICR lines. Embodiments of the present disclosure are directed to this and other considerations.